GEM 3390

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Lecture 29 Content

• Geographic Information Systems (GIS)
• Data Quality

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(No Transcript)
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• Introduction
• fact
• The quality of data is critical to judging the
  applications for which they are appropriate
• fact
• When spatial analyses are done manually using map
  overlays, users quickly learn to shift the map
  slightly to align boundaries that should overlap
  for the work at hand
• this type of informal assessment of data quality
  must be made explicit so that they can be
  properly addressed
• mis-alignment caused by positional error is one
  of several data quality issues to be taken into
  account in using and maintaining GIS data

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• the more vigorous the data quality testing, the
  more costly it becomes
• it is recommended that the level of testing
  should be balanced against the cost of the
  consequences of less accurate data or a less
  rigorous confirmed level of quality
• Data Quality standards, appropriately defined,
  tested and reported can protect both the producer
  and the user of geographic information
• when data is provided in a standard format and at
  a defined and acceptable level of quality, the
  producer is protected from liability in the case
  of inappropriate use
• Such standards also protect the user from relying
  on inappropriate information
• Presently there is a lot of research into
  national GIS and georesource integration

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• Components of Data Quality
• the characteristics that affect the usefulness of
  data can be divided into nine (9) components,
  which are grouped into 3 categories
• Micro Level Components
• Macro Level Components
• Usage Components

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• Micro Level Components
• indicates data quality factors that pertain to
  the individual data elements
• these components are usually evaluated by
  statistical testing of the data product against
  an independent source of higher quality
  information
• Include
• Positional Accuracy
• Attribute Accuracy
• Logical Accuracy
• Resolution

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• Positional Accuracy
• the expected deviance in the geographic location
  of an object in the data set (e.g. on a map) from
  its true ground position
• it is usually tested by selecting a specific
  sample of points in a prescribed manner and
  comparing the position coordinates with an
  independent and more accurate source of
  information
• two components of positional accuracy
• the Bias
• the Precision

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• Bias
• The bias refers to the systematic discrepancies
  between the represented and true position.
• ideally the bias should be zero
• commonly measured by the mean or average
  positional error of the sample points

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• Precision
• Refers to the dispersion of the positional
  errors of the data elements.
• commonly estimated by calculating the standard
  deviation of the selected test points
• a low standard deviation indicates that the
  dispersion of the positional errors is narrow
  (relatively small)
• the higher the precision of the measurement, the
  greater the confidence in using the data

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• Attribute Accuracy
• attributes may be
• Discrete
• a variable can take on only a finite number
  of values (e.g. land use classes)
• Continuous
• a variable which can take any number of
  values (e.g. ranking of data, temperature)

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• method of assessing accuracy for continuous
  variables is similar to the method for positional
  accuracy
• the assessment of the accuracy of discrete
  variables is the domain of classification
  accuracy assessment (complex assessment)
• the difficulties in assessing classification
  accuracy arise because accuracy measurement is
  significantly affected by such factors as
• the number of classes,
• the shape and size of individual areas,
• the way test points are selected, and
• the classes that are confused with each other

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• Logical Consistency
• refers to how well logical relations among data
  elements are maintained
• e.g. it would not be consistent to map some
  forest stand boundaries to the center of adjacent
  roads, and some on the edge. All are normally
  mapped to the road edge.
• ? which conforms with political and
  administrative delineations
• ? being logically consistent

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• there are cases where logical inconsistency is
  unavoidable e.g. water level at reservoirs
  fluctuates through the year
• To solve this problem a standard outline for each
  reservoir is delineated
• overlaying data sets will create a thin unique
  area termed as SLIVERS
• no standard measure for logical consistency
• logical consistency is best dealt with at the
  initial digitization stage where check plots are
  used to correct errors

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• Resolution (spatial resolution)
• smallest discernable unit or the smallest unit
  represented e.g. satellite image pixel size
• decision of how small an object to include in a
  map is made during the map compilation process
• Factors to be considered
• expected use of the map
• Legibility
• source data accuracy
• drafting expense
• camera system resolution is reported in lines/mm
  while
• digital scanning systems resolution is reported
  as the size of
• the picture elements or pixel of which the image
  is composed

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• The End